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  <title>Release Notes 3.5 &mdash; Vitis™ AI 3.5 documentation</title>
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              <p class="caption" role="heading"><span class="caption-text">Setup and Install</span></p>
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<p class="caption" role="heading"><span class="caption-text">Related AMD Solutions</span></p>
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<li class="toctree-l1"><a class="reference external" href="https://github.com/Xilinx/DPU-PYNQ">DPU-PYNQ</a></li>
<li class="toctree-l1"><a class="reference external" href="https://xilinx.github.io/finn/">FINN &amp; Brevitas</a></li>
<li class="toctree-l1"><a class="reference external" href="https://xilinx.github.io/inference-server/">Inference Server</a></li>
<li class="toctree-l1"><a class="reference external" href="https://github.com/amd/UIF">Unified Inference Frontend</a></li>
<li class="toctree-l1"><a class="reference external" href="https://ryzenai.docs.amd.com/en/latest/">Ryzen™ AI Developer Guide ~July 29</a></li>
<li class="toctree-l1"><a class="reference external" href="https://onnxruntime.ai/docs/execution-providers/community-maintained/Vitis-AI-ExecutionProvider.html">Vitis™ AI ONNX Runtime Execution Provider</a></li>
<li class="toctree-l1"><a class="reference external" href="https://xilinx.github.io/VVAS/">Vitis™ Video Analytics SDK</a></li>
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  <section id="release-notes-3-5">
<h1>Release Notes 3.5<a class="headerlink" href="#release-notes-3-5" title="Permalink to this heading">¶</a></h1>
<section id="version-compatibility">
<h2>Version Compatibility<a class="headerlink" href="#version-compatibility" title="Permalink to this heading">¶</a></h2>
<p>Vitis™ AI v3.5 and the DPU IP released with the v3.5 branch of this repository are verified as compatible with Vitis, Vivado™, and PetaLinux version 2023.1. If you are using a previous release of Vitis AI, you should review the <a class="reference internal" href="version_compatibility.html#version-compatibility"><span class="std std-ref">version compatibility matrix</span></a> for that release.</p>
</section>
<section id="documentation-and-github-repository">
<h2>Documentation and Github Repository<a class="headerlink" href="#documentation-and-github-repository" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Merged UG1333 into UG1414</p></li>
<li><p>Streamlined UG1414 to remove redundant content</p></li>
<li><p>Streamlined UG1414 to focus exclusively on core tool usage.  Core tools such as the Optimizer, Quantizer and Compiler are now being utilized across multiple targets (ie Ryzen™ AI, EPYC™) and this change seeks to make UG1414 more portable to these targets</p></li>
<li><p>Migrated Adaptive SoC and Alveo specific content from UG1414 to Github.IO</p></li>
<li><p>New Github.IO Toctree structure</p></li>
<li><p>Integrated VART Runtime APIs in Doxygen format</p></li>
</ul>
</section>
<section id="docker-containers-and-gpu-support">
<h2>Docker Containers and GPU Support<a class="headerlink" href="#docker-containers-and-gpu-support" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Removed Anaconda dependency from TensorFlow 2 and PyTorch containers in order to address Anaconda commercial license requirements</p></li>
<li><p>Updated Docker container to disable Ubuntu 18.04 support (which was available in Vitis AI but not officially supported).  This was done to address <a class="reference external" href="https://nvd.nist.gov/vuln/detail/CVE-2021-3493">CVE-2021-3493</a>.</p></li>
</ul>
</section>
<section id="model-zoo">
<h2>Model Zoo<a class="headerlink" href="#model-zoo" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Added more classic models without modification such as YOLO series and 2D Unet</p></li>
<li><p>Provided model info card for each model and Jupyter Notebook tutorials for new models</p></li>
<li><p>New copyleft repo for GPL license models</p></li>
</ul>
</section>
<section id="onnx-cnn-quantizer">
<h2>ONNX CNN Quantizer<a class="headerlink" href="#onnx-cnn-quantizer" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Initial release</p></li>
<li><p>This is a new quantizer that supports the direct PTQ quantization of ONNX models for DPU. It is a plugin built for the ONNXRuntime native quantizer.</p></li>
<li><p>Support for power-of-two quantization with both QDQ and QOP format.</p></li>
<li><p>Support for Non-overflow and Min-MSE quantization methods.</p></li>
<li><p>Support for various quantization configurations in power-of-two quantization in both QDQ and QOP format.</p></li>
<li><p>Support for signed and unsigned configurations.</p></li>
<li><p>Support for symmetry and asymmetry configurations.</p></li>
<li><p>Support for per-tensor and per-channel configurations.</p></li>
<li><p>Support for ONNX models in excess of 2GB.</p></li>
<li><p>Support for the use of the CUDAExecutionProvider for calibration in quantization.</p></li>
</ul>
</section>
<section id="pytorch-cnn-quantizer">
<h2>PyTorch CNN Quantizer<a class="headerlink" href="#pytorch-cnn-quantizer" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Support for Pytorch 1.13 and 2.0</p></li>
<li><p>Support for mixed precision quantization, float32/float16/bfloat16/intx</p></li>
<li><p>Support for bit-wise accuracy cross check between quantizer and ONNX-runtime</p></li>
<li><p>Split and chunk operators were automatically converted to slicing</p></li>
<li><p>Dict input/output supports for model forward function</p></li>
<li><p>Keywords argument supports for model forward function</p></li>
<li><p>Support for matmul subroutine</p></li>
<li><p>Added support for BFP data type quantization</p></li>
<li><p>QAT supports training on mutiple GPUs</p></li>
<li><p>QAT supports operations with multiple inputs or outputs</p></li>
</ul>
</section>
<section id="tensorflow-2-cnn-quantizer">
<h2>TensorFlow 2 CNN Quantizer<a class="headerlink" href="#tensorflow-2-cnn-quantizer" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Updated to support for Tensorflow 2.12 and Python 3.8.</p></li>
<li><p>Support for quantizing subclass models.</p></li>
<li><p>Support for mix precision, supports layer-wise data type configuration, supports float32, float16, bfloat16, and int quantization.</p></li>
<li><p>Support for BFP datatypes, and add a new quantize strategy called ‘bfp’.</p></li>
<li><p>Support for quantize Keras nested models.</p></li>
<li><p>Experimental support for quantizing the frozen pb format model in TensorFlow 2.x.</p></li>
<li><p>Added a new ‘gpu’ quantize strategy which uses float scale quantization and is used in GPU deployment scenarios.</p></li>
<li><p>Support for exporting the quantized model to frozen pb format or onnx format.</p></li>
<li><p>Support for exporting the quantized model with power-of-two scales to frozen pb format with “FixNeuron” inside, to be compatible with some compilers with pb format input.</p></li>
<li><p>Support for splitting Avgpool and Maxpool with large kernel sizes into smaller kernel sizes.</p></li>
</ul>
<p>Bug Fixed:
1.      Fixed a gradient bug in the ‘pof2s_tqt’ quantize strategy.
2.      Fixed a bug of quantization position change introduced by the fast fine-tuning process after the PTQ.
3.      Fixed a graph transformation bug when a TFOpLambda op has multiple inputs.</p>
</section>
<section id="tensorflow-1-cnn-quantizer">
<h2>TensorFlow 1 CNN Quantizer<a class="headerlink" href="#tensorflow-1-cnn-quantizer" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Support for fast fine-tuning that improves PTQ accuracy.</p></li>
<li><p>Support for folding Reshape and ResizeNearestNeighbor operators.</p></li>
<li><p>Support for splitting Avgpool and Maxpool with large kernel sizes into smaller kernel sizes.</p></li>
<li><p>Support for quantizing Sum, StridedSlice, and Maximum operators.</p></li>
<li><p>Support for setting the input shape of the model, which is useful in the deployment of models with undefined input shapes.</p></li>
<li><p>Support for setting the opset version in exporting onnx format.</p></li>
</ul>
<p>Bug Fixed:
1.      Fixed a bug where the AddV2 operation is misinterpreted as a BiasAdd.</p>
</section>
<section id="compiler">
<h2>Compiler<a class="headerlink" href="#compiler" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>New operators supported: Broadcast add/mul, Bilinear downsample, Trilinear downsample, Group conv2d, Strided-slice</p></li>
<li><p>Performance improved on XV2DPU</p></li>
<li><p>Error message improved</p></li>
<li><p>Compilation time speed up</p></li>
</ul>
</section>
<section id="pytorch-optimizer">
<h2>PyTorch Optimizer<a class="headerlink" href="#pytorch-optimizer" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Removed requirement for license purchase</p></li>
<li><p>Migrated to Github open-source</p></li>
<li><p>Support for PyTorch 1.11, 1.12 and 1.13</p></li>
<li><p>Support for pruning of grouped convolution</p></li>
<li><p>Support for setting the number of channels to be a multiple of the specified number after pruning</p></li>
</ul>
</section>
<section id="tensorflow-2-optimizer">
<h2>TensorFlow 2 Optimizer<a class="headerlink" href="#tensorflow-2-optimizer" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Removed requirement for license purchase</p></li>
<li><p>Migrated to Github open-source</p></li>
<li><p>Support for TensorFlow 2.11 and 2.12</p></li>
<li><p>Support for pruning of tf.keras.layers.SeparableConv2D</p></li>
<li><p>Fixed tf.keras.layers.Conv2DTranspose pruning bug</p></li>
<li><p>Support for setting the number of channels to be a multiple of the specified number after pruning</p></li>
</ul>
</section>
<section id="runtime">
<h2>Runtime<a class="headerlink" href="#runtime" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Supports Versal AI Edge VEK280 evaluation kit</p></li>
<li><p>Buffer optimized for multi-batches to improve performance</p></li>
<li><p>Added new tensor buffer interface to enhance zero copy</p></li>
</ul>
</section>
<section id="vitis-onnx-runtime-execution-provider-voe">
<h2>Vitis ONNX Runtime Execution Provider (VOE)<a class="headerlink" href="#vitis-onnx-runtime-execution-provider-voe" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Support for ONNX Opset version 18, ONNX Runtime 1.16.0 and ONNX version 1.13</p></li>
<li><p>Support for both C++ and Python APIs(Python version 3)</p></li>
<li><p>Support for Vitis AI EP and other EPs to work together to deploy the model</p></li>
<li><p>Provided Onnx examples based on C++ and Python APIs</p></li>
<li><p>Vitis AI EP is open sourced and upstreamed to ONNX public repo on Github</p></li>
</ul>
</section>
<section id="library">
<h2>Library<a class="headerlink" href="#library" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Added three new model libraries and support for five additional models</p></li>
</ul>
</section>
<section id="model-inspector">
<h2>Model Inspector<a class="headerlink" href="#model-inspector" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Added support for DPUCV2DX8G</p></li>
</ul>
</section>
<section id="profiler">
<h2>Profiler<a class="headerlink" href="#profiler" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Added Profiler support for DPUCV2DX8G</p></li>
</ul>
</section>
<section id="dpu-ip-versal-aie-ml-targets-dpucv2dx8g-versal-ai-edge-core">
<h2>DPU IP - Versal AIE-ML Targets DPUCV2DX8G (Versal AI Edge / Core)<a class="headerlink" href="#dpu-ip-versal-aie-ml-targets-dpucv2dx8g-versal-ai-edge-core" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>General access release for the Versal AI Edge device VE2802, Versal AI Core device VC2802 and Alveo V70 card</p></li>
<li><p>Configurable from C20B1 to C20B14</p></li>
<li><p>Support most 2D operators based on models in the Model Zoo</p></li>
</ul>
</section>
<section id="dpu-ip-zynq-ultrascale-dpuczdx8g">
<h2>DPU IP - Zynq Ultrascale+ DPUCZDX8G<a class="headerlink" href="#dpu-ip-zynq-ultrascale-dpuczdx8g" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>No DPU IP updates in  3.5 release</p></li>
<li><p>No DPU reference design updates in 3.5 release</p></li>
<li><p>No pre-built board image updates in 3.5 release</p></li>
</ul>
</section>
<section id="dpu-ip-versal-aie-targets-dpucvdx8g">
<h2>DPU IP - Versal AIE Targets DPUCVDX8G<a class="headerlink" href="#dpu-ip-versal-aie-targets-dpucvdx8g" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>No DPU IP updates in 3.5 release</p></li>
<li><p>No DPU reference design updates in 3.5 release</p></li>
<li><p>No pre-built board image updates in 3.5 release</p></li>
</ul>
</section>
<section id="dpu-ip-cnn-alveo-data-center-dpucvdx8h">
<h2>DPU IP - CNN - Alveo Data Center DPUCVDX8H<a class="headerlink" href="#dpu-ip-cnn-alveo-data-center-dpucvdx8h" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>No DPU IP updates in 3.5 release</p></li>
<li><p>No DPU reference design  updates in 3.5 release</p></li>
<li><p>No pre-built board image updates in 3.5 release</p></li>
</ul>
</section>
<section id="wego">
<h2>WeGO<a class="headerlink" href="#wego" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Support for Alveo V70 DPU GA release.</p></li>
<li><p>Support for PyTorch 1.13.1 and TensorFlow r2.12.</p></li>
<li><p>Enhanced WeGO-Torch to support PyTorch 2.0 as a preview feature.</p></li>
<li><p>Introduced new C++ API that supports for WeGO-Torch</p></li>
<li><p>Implemented WeGO-TF1 and WeGO-TF2 as out-of-tree plugins.</p></li>
</ul>
</section>
<section id="known-issues">
<h2>Known Issues<a class="headerlink" href="#known-issues" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>To be announced ASAP</p></li>
</ul>
<p>AMD, the AMD Arrow logo, and combinations thereof are trademarks of Advanced Micro Devices, Inc.</p>
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